Every Power BI Developer pays attention to the underlying performance of their dashboard beyond just churning out reports. This is why adhering to best practices rules should no longer feel like a task, irrespective of your level of experience with Power BI. Semantic Link Labs is here to get the job done.
If you are reading about Semantic Link Labs for the first time, you can read my demo introductory article here. This is a Python library developed by Michael Kovalsky that makes this process easier with just a few lines of code in Microsoft Fabric Notebook.
Our Use Case:
David, A Power BI developer at the AMA enterprise, got several complaints from colleagues that the Sales and Return dashboards take too long to load and sometimes time out. David thought of many reasons why this could have happened, but he wants to start by using the Model Best Practice Analyser Method and Vertipaq Analyzer to investigate the issue.
Before this day, David knew he could use an external tool like Tabular Editor for this task. But he recently found out about a new Python library called Semantic Link Lab using Microsoft Fabric Notebook that can help him figure out this issue with the report with just a few lines of code.
The sample Sales & Returns Sample v201912 dashboard use for this demo is available for download here
You can also download this Microsoft Fabric Notebook used for this Demo here.
David’s Task:
- Perform a Model Best Practice Analyser (BPA) on the Semantic Model of your Power BI report.
- Perform a Vertipaq Analyzer on your semantic model.
Let’s get started:
Open your Notebook In Microsoft Fabric, let's investigate what went wrong.
Before we get started, we must make sure we have the Semantic Link Labs library installed as well as the other necessary libraries.
#Install the Semantic link Labs library in your Fabric notebook %pip install semantic-link-labs
After that, we will also be installing the necessary libraries in your notebook.
### Once installed, run this code to import the library into your notebook import sempy_labs as labs from sempy_labs import report as rep from sempy_labs.report import ReportWrapper
1. Performance Model Best Practice Analyzer (BPA) on a Semantic Model report.
Model Best Practice Analyzer uses best practice rules to scan your semantic models for bad DAX code, improper formatting and usage of data types, inappropriate relationships, and potentially risky calculation patterns that should be fixed in your report.
#Model Best Practice Analyzer import sempy_labs as labs # Enter the name or ID of your semantic model dataset = 'Sales & Returns Sample v201912' # Enter the name or ID of the workspace in which the semantic model # resides workspace = 'Fabricday' labs.run_model_bpa(dataset=dataset, workspace=workspace)
When you toggle on the results below, we can see that we have:
- 14 DAX expression with high rule violation severity, 10on Medium severity, report pages with other details in the Sales & Return Sample v201912 Dashboard.
- 64 Formatting Medium rule violation severity and 41 on low-level/hints severity.
- 146 Maintenance low-level/hints rule violation severity and 10 Medium rule violation severity.
- 64 Performance Medium rule violation severity.
2. Perform a Vertipaq Analyzer on your Semantic Model.
import sempy_labs as labs # Enter the name or ID of your semantic model dataset = 'Sales & Returns Sample v201912' # Enter the name or ID of the workspace in which the semantic model # resides workspace = 'Fabricday' #x = labs.vertipaq_analyzer(dataset=dataset, workspace=workspace) # Setting export='table' will export the results to delta tables in # the lakehouse attached to the notebook x = labs.vertipaq_analyzer(dataset=dataset, workspace=workspace, export='table') # Setting export='zip' will export the results to a .zip file in the lakehouse attached to the notebook. # x = labs.vertipaq_analyzer(dataset=dataset, workspace=workspace, export='zip')
The result below shows that the Vertipaq Analyzer Analysis of the Sales & Returns Sample v201912 has been exported to a delta table in my Lakehouse.
A snapshot of the Vertipaq Analyzer in my Lakehouse. This shows the Vertipaq Analyzer columns, hierarchies, tables, model, partitions, relationships and tables which can be visualize to understand the semantic model performances.
Image shows the saved Vertipaq Analyzer result in the Lakehouse
Conclusion
If you ever have a slow Power BI Dashboard that takes time to load, and you want to understand the reason for the slow performance. You can download the Microsoft Fabric Notebook used for this optimisation and enhancement Power BI report with Semantic Link Labs here.
References
- Semantic Link Labs Git Hub Repository by Michael Kovalsky: https://github.com/microsoft/semantic-link-labs
Feel free to connect with me via my socials below if you want to discuss further.
LinkedIn: Musili Adebayo
Twitter: Musili_Adebayo
